Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-implemented method for predicting a probability of future pirate activity in a geographical area of interest, comprising: receiving, at a computer programmed with appropriate software, INTEL data representative of intelligence information relating to at least one pirate group in the geographical area of interest, the INTEL data including data of at least one of a meteorological and a climatological threshold associated with the pirate group; receiving, at the computer, METOC data representative of meteorological and climatological information associated with the geographical area of interest; generating, at the computer, data of a plurality of model pirate trajectories for the pirate group, each trajectory having a specific associated set of INTEL and METOC attributes for the pirate group; compiling, at the computer, the data of the model pirate trajectories; and calculating a probability distribution of a presence of the pirate group in the geographical area of interest for at least one future time period based on the compiled model pirate trajectories.
A computer system predicts the probability of pirate attacks in a specific area. It takes intelligence data about pirate groups, including their weather-related preferences (like wave, wind, or current thresholds). The system also gets weather and climate data for the area. It then creates many possible routes for the pirates, each considering the pirate group's intel and the weather conditions. These routes are compiled, and the system calculates the probability of the pirates being present in the area at a future time, based on these routes.
2. The method according to claim 1 , wherein the meteorological and climatological threshold associated with the pirate group includes at least one of a wave, wind, and current threshold for the pirate group and the METOC data includes at least one of wave data, wind data, and current data in the geographical area of interest; wherein the probability distribution is based on at least one of a wave, wind, and current affecting the pirate group in the geographical area of interest.
Building upon the pirate attack prediction system, the system uses specific weather thresholds for pirate groups like wave, wind, and current preferences along with corresponding weather data (wave, wind, and current data in the area). The probability calculation of pirate presence is specifically based on how waves, wind, and currents affect the pirates in the target area. Therefore, If pirates prefer calm seas, the system factors in calm sea probabilities when predicting their location.
3. The method according to claim 1 , further comprising: receiving, at the computer, data of an actual pirate observation in the geographical area of interest; and generating a new probability distribution of future pirate activity based on a prior probability distribution and the actual pirate observation.
In addition to the basic pirate activity prediction, this system also takes in real-time pirate sightings. When a sighting is reported, the system updates its probability calculation. It uses the previous probability distribution and the new sighting to generate an updated, more accurate prediction of future pirate activity. For example, an area with low predicted activity may suddenly spike in predicted pirate activity if a new sighting occurs there.
4. A computer-implemented method for predicting a probability of a future activity affected by meterological conditions in a geographical area of interest, comprising: receiving, at a computer programmed with appropriate software, INTEL data representative of intelligence information relating to at least one participant in the activity affected by meterological conditions, the INTEL data including data of at least one of a meteorological and a climatological threshold associated with the participant; receiving, at the computer, METOC data representative of meteorological and climatological information associated with the geographical area of interest; generating, at the computer, data of a plurality of model participant trajectories for the participant, each trajectory having a specific associated set of INTEL and METOC attributes for the participant; compiling, at the computer, the data of the model participant trajectories; generating, at the computer, a probability distribution of the participant's presence in the geographical area of interest for at least one future time period based on the compiled model participant trajectories; generating, at the computer, a probability distribution of a presence of meteorological conditions suitable for the participant to engage in the activity for the at least one future time period; and generating, at the computer, a probability distribution of an occurrence of the activity in the at least one future time period based on the probability distribution of the participant's presence and the probability distribution of suitable meteorological conditions in the geographical area of interest.
This computer system predicts the probability of a general activity (not just piracy) that's affected by weather in a specific area. It requires intelligence data about participants in the activity, including their weather preferences. It also needs weather and climate data for the area. The system simulates many possible routes for the participants, each route considering intel and weather. These routes are compiled. The system then calculates the probability of the participant being present in the area, and the probability of suitable weather conditions existing for them to engage in their activity. Finally, it combines these probabilities to predict the likelihood of the activity occurring.
5. The method according to claim 4 , further comprising generating, at the computer, a risk distribution indicative of the risk of the activity occurring in the geographical area.
Building on the weather-affected activity prediction, the system goes a step further and generates a risk distribution. This risk distribution indicates the level of danger associated with the activity occurring in the geographical area. It goes beyond predicting probability, and attempts to quantify the risk involved.
6. The method according to claim 4 , further comprising: receiving, at the computer, data of an actual participant observation in the geographical area of interest; and generating a new probability distribution of the activity based on a prior probability distribution and the actual participant observation.
Expanding on the general weather-affected activity prediction system, this system incorporates real-time observations. When an actual sighting of the participant involved in the activity is received, the system updates its probability calculations. It takes the prior probability distribution and the new sighting and produces a revised probability distribution of the activity.
7. A computer-implemented method for predicting a risk to a vulnerable activity in a geographical area of interest, comprising: receiving, at a computer programmed with appropriate software, INTEL data representative of intelligence information relating to at a threat activity in the geographical area of interest, the INTEL data including data of at least one of a meteorological and a climatological threshold associated with a threat participant in the threat activity; receiving, at the computer, METOC data representative of meteorological and climatological information associated with the geographical area of interest; receiving, at the computer, data representative of a vulnerable activity in the geographical area, the data including data of at least one of a meteorological and a climatological threshold associated with the vulnerable activity and further including data associated with a vulnerability of the vulnerable activity to the threat activity; generating, at the computer, data of a plurality of model threat participant trajectories for the threat activity, each trajectory having a specific associated set of INTEL and METOC attributes for the threat participant; compiling, at the computer, the data of the model participant trajectories for the threat participant; generating, at the computer, a probability distribution of the presence of the threat activity in the geographical area of interest for a future time period based on the compiled model threat participant trajectories; generating, at the computer, a probability distribution of a presence of meteorological conditions suitable for the vulnerable activity for the future time period; generating, at the computer, a probability distribution of the vulnerable activity in the geographical area of interest at the future time period, the probability distribution being based on the meteorological and climatological threshold associated with the vulnerable activity at the meteorological conditions at the future time period; and generating, at the computer, a probability distribution of the risk to the vulnerable activity from the threat activity based on the probability distribution of the vulnerable activity and the probability distribution of the threat activity.
This invention relates to a computer-implemented method for predicting risks to vulnerable activities in a specific geographical area by analyzing intelligence (INTEL) data, meteorological and climatological (METOC) data, and vulnerability characteristics. The method processes INTEL data about potential threat activities, including meteorological or climatological thresholds relevant to threat participants. It also receives METOC data for the area of interest and data about vulnerable activities, including their meteorological or climatological thresholds and their susceptibility to threats. The system generates multiple model threat participant trajectories, each with specific INTEL and METOC attributes, and compiles these trajectories to create a probability distribution of the threat activity's presence in the future. Simultaneously, it generates a probability distribution of favorable meteorological conditions for the vulnerable activity and another distribution of the vulnerable activity's presence based on these conditions. Finally, the method combines these distributions to produce a probability distribution of the risk to the vulnerable activity from the threat activity. This approach enables proactive risk assessment by integrating threat intelligence, environmental factors, and vulnerability data.
8. The method according to claim 7 , wherein the threat activity is pirate activity and the threat participant comprises a pirate group.
Within the vulnerable activity risk prediction system, the "threat activity" is specified as pirate activity, and the "threat participant" is a pirate group. This means the system is specifically tailored to predict the risk to something from pirates.
9. The method according to claim 7 , wherein the vulnerable activity is a maritime activity in the geographical area of interest.
Using the vulnerable activity risk prediction method, the "vulnerable activity" is specified as a maritime activity in the geographical area. This means the system is designed to predict the risk to activities happening at sea.
10. The method according to claim 9 , wherein the maritime activity comprises shipping activity.
Within the system predicting risk to maritime activity, the maritime activity is further defined as shipping activity. Therefore, the system is specifically intended to predict risks to ships from a threat.
11. The method according to claim 7 , wherein the meteorological and climatological threshold associated with the threat participant includes at least one of a wave, wind, and current threshold and the METOC data includes at least one of wave data, wind data, and current data; wherein the probability distribution is based on at least one of a wave, wind, and current affecting the threat participant in the geographical area of interest.
In the vulnerable activity risk prediction method, the weather preferences of the threat participant (like a pirate group) are wave, wind, and current thresholds. The weather data includes wave, wind, and current data. The probability distribution is based on how these weather elements affect the threat participant in the area.
12. The method according to claim 7 , further comprising: receiving, at the computer, data of an actual observation of the threat activity in the geographical area of interest; and generating a new probability distribution of a risk of the threat activity based on a prior probability distribution of risk and the actual threat observation.
Further improving on the vulnerable activity risk prediction, the system incorporates real-time threat observations. When an actual sighting of the threat activity is reported, the system updates the risk probability calculation. It uses the previous risk probability and the new threat observation to generate an updated probability distribution of risk.
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September 16, 2014
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